Do-Yeon Kang
Papers
1
Total Citations
52
H-Index
1
About
Dr. Do-Yeon Kang is a leading researcher in wearable robotics and human-robot interaction, whose work focuses on enabling intelligent, adaptive control for assistive exoskeletons. Their most-cited study, "Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks" (2022, 52 citations), represents a pivotal contribution to the field. In this work, Dr. Kang developed a deep learning framework that fuses inertial measurement unit (IMU) and encoder sensor data to recognize human activities in real time, allowing exoskeletons to dynamically adjust their support for daily tasks. This innovation directly addresses the critical challenge of seamless human-robot collaboration, moving beyond static assistance toward context-aware, responsive control. By integrating sensor fusion with neural networks, Dr. Kang’s research enhances the practicality and safety of wearable exoskeletons for rehabilitation and occupational use. With growing citation impact, their work is shaping the next generation of intelligent assistive devices, bridging the gap between robotic capability and natural human movement. Dr. Kang’s contributions are essential reading for researchers in biomechatronics, deep learning for robotics, and human augmentation technologies.
Research Focus
Key Achievements
Top Papers
- 1